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A hydrological sensor web ontology based on the SSN ontology: A case study for a flood / Chao Wang in ISPRS International journal of geo-information, vol 7 n° 1 (January 2018)
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[article]
Titre : A hydrological sensor web ontology based on the SSN ontology: A case study for a flood Type de document : Article/Communication Auteurs : Chao Wang, Auteur ; Nengcheng Chen, Auteur ; Wei Wang, Auteur ; Zeqiang Chen, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] classe d'objets
[Termes IGN] données spatiotemporelles
[Termes IGN] hydrographie
[Termes IGN] modèle d'ontologie
[Termes IGN] ontologie
[Termes IGN] raisonnement sémantique
[Termes IGN] réseau de capteursRésumé : (Auteur) Accompanying the continuous development of sensor network technology, sensors worldwide are constantly producing observation data. However, the sensors and their data from different observation platforms are sometimes difficult to use collaboratively in response to natural disasters such as floods for the lack of semantics. In this paper, a hydrological sensor web ontology based on SSN ontology is proposed to describe the heterogeneous hydrological sensor web resources by importing the time and space ontology, instantiating the hydrological classes, and establishing reasoning rules. This work has been validated by semantic querying and knowledge acquiring experiments. The results demonstrate the feasibility and effectiveness of the proposed ontology and its potential to grow into a more comprehensive ontology for hydrological monitoring collaboratively. In addition, this method of ontology modeling is generally applicable to other applications and domains. Numéro de notice : A2018-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7010002 En ligne : https://doi.org/10.3390/ijgi7010002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89261
in ISPRS International journal of geo-information > vol 7 n° 1 (January 2018)[article]
Titre : Image processing in agriculture and forestry Type de document : Monographie Auteurs : Gonzalo Pajares Martinsanz, Éditeur scientifique ; Francisco Rovira-Más, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2018 Importance : 222 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 9783038970972 9783038970989 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] changement d'occupation du sol
[Termes IGN] chlorophylle
[Termes IGN] couvert forestier
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] indice foliaire
[Termes IGN] instrument embarqué
[Termes IGN] phénologie
[Termes IGN] positionnement en intérieur
[Termes IGN] reconstruction 3D
[Termes IGN] teneur en eau de la végétation
[Termes IGN] traitement automatique de données
[Termes IGN] vision par ordinateurRésumé : (édition) Image processing in agriculture and forestry represents a challenge towards the automation of tasks for better performances. Agronomists, computer and robotics engineers, and agricultural machinery industry manufacturers now have at their disposal a book containing a collection of methods, procedures, designs, and descriptions at the technological forefront, which serves as an important support and aid for the implementation and development of their own ideas.The book describes: (1) Applications (canopy on trees, aboveground biomass, phenotyping, chlorophyll, leaf area index, water and nutrient content, land cover change, soil properties, and secure autonomous navigation); (2) Imaging devices onboard robots, unmanned aerial vehicles (UAVs), and satellites operating at different spectral ranges (visible, infrared, hyper-multispectral bands, and radar), as well as guidelines for selecting machine vision systems in outdoor environments; and (3) (Specific computer vision methods (generic and convolutional neural networks, machine learning, specific segmentation approaches, vegetation indices, and three-dimensional (3D) reconstruction). Note de contenu : Preface
1- Machine-vision systems selection for agricultural vehicles
2- Precise navigation of small agricultural robots in sensitive areas with a smart plant camera
3- Using deep learning to challenge safety standard for highly autonomous machines in agriculture
4- 3D reconstruction of plant/tree canopy using monocular and binocular vision
5- Peach flower monitoring using aerial multispectral imaging
6- Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks
7- Non-parametric retrieval of aboveground biomass in Siberian boreal forests with ALOS PALSAR interferometric coherence and backscatter intensity
8- Imaging for high-throughput phenotyping in energy sorghum
9- Viewing geometry sensitivity of commonly used vegetation indices towards the estimation of biophysical variables in orchards
10- Estimating mangrove biophysical variables using WorldView-2 satellite data: Rapid creek, Northern Territory, Australia
11- Land cover change image analysis for Assateague Island National Seashore following hurricane Sandy
12- Automated soil physical parameter assessment using smartphone and digital camera imageryNuméro de notice : 25921 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Monographie En ligne : https://doi.org/10.3390/books978-3-03897-098-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96137
Titre : Introduction to Deep Learning : From Logical Calculus to Artificial Intelligence Type de document : Monographie Auteurs : Sandro Skansi, Auteur Editeur : Springer Nature Année de publication : 2018 Importance : 196 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-73004-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] codage
[Termes IGN] estimation par noyau
[Termes IGN] matrice de covariance
[Termes IGN] Perceptron multicouche
[Termes IGN] Python (langage de programmation)
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau neuronal convolutif
[Termes IGN] sciences cognitives
[Termes IGN] théorie des probabilitésRésumé : (auteur) This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.
Topics and features:
Introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning
Discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network
Examines convolutional neural networks, and the recurrent connections to a feed-forward neural network
Describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning
Presents a brief history of artificial intelligence and neural networks, and reviews interesting
open research problems in deep learning and connectionism
This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.Note de contenu : 1- From Logic to Cognitive Science
2- Mathematical and Computational Prerequisites
3- Machine Learning Basics
4- Feedforward Neural Networks
5- Modifications and Extensions to a Feed-Forward Neural Network
6- Convolutional Neural Networks
7- Recurrent Neural Networks
8- Autoencoders
9- Neural Language Models
10- An Overview of Different Neural Network Architectures
11- ConclusionNuméro de notice : 25787 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-73004-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94990 Learning multiscale deep features for high-resolution satellite image scene classification / Qingshan Liu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)
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Titre : Learning multiscale deep features for high-resolution satellite image scene classification Type de document : Article/Communication Auteurs : Qingshan Liu, Auteur ; Renlong Hang, Auteur ; Huihui Song, Auteur ; Zhi Li, Auteur Année de publication : 2018 Article en page(s) : pp 117 - 126 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] image satellite
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) In this paper, we propose a multiscale deep feature learning method for high-resolution satellite image scene classification. Specifically, we first warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multiscale satellite images are fed into their corresponding SPP-nets, respectively, to extract multiscale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult data sets show that the proposed method achieves favorable performance compared with other state-of-the-art methods. Numéro de notice : A2018-185 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2743243 Date de publication en ligne : 13/09/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2743243 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89842
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 117 - 126[article]Leveraging correlation across space and time to interpolate geophysical data via CoKriging / Sonja Pravilovic in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
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Titre : Leveraging correlation across space and time to interpolate geophysical data via CoKriging Type de document : Article/Communication Auteurs : Sonja Pravilovic, Auteur ; Annalisa Appice, Auteur ; Donato Malerba, Auteur Année de publication : 2018 Article en page(s) : pp 191 - 212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse multivariée
[Termes IGN] apprentissage automatique
[Termes IGN] corrélation
[Termes IGN] données spatiotemporelles
[Termes IGN] interpolation
[Termes IGN] krigeageRésumé : (Auteur) Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time). Numéro de notice : A2018-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1381338 En ligne : https://doi.org/10.1080/13658816.2017.1381338 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89176
in International journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 191 - 212[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018011 RAB Revue Centre de documentation En réserve L003 Disponible Localisation d'objets urbains à partir de sources multiples dont des images aériennes / Lionel Pibre (2018)
PermalinkLocalisation par l'image en milieu urbain : application à la réalité augmentée / Antoine Fond (2018)
PermalinkPermalinkPermalinkMachine learning and pose estimation for autonomous robot grasping with collaborative robots / Victor Talbot (2018)
PermalinkModélisation spatio-temporelle multi-niveau à base d'ontologies pour le suivi de la dynamique en imagerie satellitaire / Fethi Ghazouani (2018)
PermalinkMulti-agents systems for cartographic generalization: Feedback from past and on-going research / Cécile Duchêne (2018)
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PermalinkOn the production of semantic and textured 3D meshes of large scale urban environments from mobile mapping images and LIDAR scans / Mohamed Boussaha (2018)
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PermalinkRéseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne / Jean Ogier du Terrail (2018)
PermalinkPermalinkPermalinkSpatial big data and machine learning in GIScience, Workshop at GIScience 2018, Melbourne, Australia, 28 August 2018 / Martin Raubal (2018)
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PermalinkSpatio-temporal grid mining applied to image classification and cellular automata analysis / Romain Deville (2018)
PermalinkSuperpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks / Tristan Postadjian (2018)
PermalinkSuperPoint Graph : segmentation sémantique de nuages de points LiDAR à grande échelle / Loïc Landrieu (2018)
PermalinkPermalinkToponym matching through deep neural networks / Rui Santos in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
PermalinkUtilisation de véhicules traceurs pour la détection et la localisation de l'infrastructure routière par apprentissage automatique / Yann Méneroux (2018)
PermalinkObject-based classification of terrestrial laser scanning point clouds for landslide monitoring / Andreas Mayr in Photogrammetric record, vol 32 n° 160 (December 2017)
PermalinkAbove-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China / Ran Jing in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)
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